Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

Abstract

Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable&#x2019;s sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6&#x0025; accuracy rate, 0.15sec lowest execution time, and 0.095&#x0025; maximum error rate for fault location at fractional factor (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>) equals to 0.8.